def test_xi_broadcast(self):
# verify that the interpolators broadcast xi
x, y, values = self._sample_2d_data()
points = (x, y)
xi = cp.linspace(0, 1, 2)
yi = cp.linspace(0, 3, 3)
for method in ["nearest", "linear"]: # 'splinef2d']:
sample = (xi[:, None], yi[None, :])
v1 = interpn(points,
values,
sample,
method=method,
bounds_error=False)
assert_equal(v1.shape, (2, 3))
xx, yy = np.meshgrid(xi, yi)
sample = cp.c_[xx.T.ravel(), yy.T.ravel()]
v2 = interpn(points,
values,
sample,
method=method,
bounds_error=False)
assert_allclose(v1, v2.reshape(v1.shape))
def test_nonscalar_values(self):
# Verify that non-scalar valued values also works
points, values = self._sample_4d_data()
np.random.seed(1234)
values = cp.asarray(np.random.rand(3, 3, 3, 3, 6))
sample = cp.asarray(np.random.rand(7, 11, 4))
for method in ["nearest", "linear"]:
v = interpn(points,
values,
sample,
method=method,
bounds_error=False)
assert_equal(v.shape, (7, 11, 6), err_msg=method)
vs = [
interpn(
points,
values[..., j],
sample,
method=method,
bounds_error=False,
) for j in range(6)
]
v2 = cp.asarray(vs).transpose(1, 2, 0)
assert_allclose(v, v2, err_msg=method)
def test_xi_1d(self):
# verify that 1-D xi works as expected
points, values = self._sample_4d_data()
sample = cp.asarray([0.1, 0.1, 10.0, 9.0])
v1 = interpn(points, values, sample, bounds_error=False)
v2 = interpn(points, values, sample[None, :], bounds_error=False)
assert_allclose(v1, v2)
def test_list_input(self):
x, y, z = self._sample_2d_data()
xi = cp.asarray([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
for method in ["nearest", "linear"]: # 'splinef2d']:
v1 = interpn((x, y), z, xi, method=method)
v2 = interpn((x.tolist(), y.tolist()),
z.tolist(),
xi.tolist(),
method=method)
assert_allclose(v1, v2, err_msg=method)
def test_complex(self):
x, y, values = self._sample_2d_data()
points = (x, y)
values = values - 2j * values
sample = cp.asarray([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
for method in ["linear", "nearest"]:
v1 = interpn(points, values, sample, method=method)
v2r = interpn(points, values.real, sample, method=method)
v2i = interpn(points, values.imag, sample, method=method)
v2 = v2r + 1j * v2i
assert_allclose(v1, v2)
def test_nearest_4d(self):
# create a 4-D grid of 3 points in each dimension
points, values = self._sample_4d_data()
interp_rg = RegularGridInterpolator(points, values, method="nearest")
sample = cp.asarray([[0.1, 0.1, 10.0, 9.0]])
wanted = interpn(points, values, sample, method="nearest")
assert_array_almost_equal(interp_rg(sample), wanted)
def test_xi_nd(self):
# verify that higher-d xi works as expected
points, values = self._sample_4d_data()
np.random.seed(1234)
sample = cp.asarray(np.random.rand(2, 3, 4))
v1 = interpn(points,
values,
sample,
method="nearest",
bounds_error=False)
assert_equal(v1.shape, (2, 3))
v2 = interpn(
points,
values,
sample.reshape(-1, 4),
method="nearest",
bounds_error=False,
)
assert_allclose(v1, v2.reshape(v1.shape))
def test_4d_nearest_outofbounds(self):
# create a 4-D grid of 3 points in each dimension
points, values = self._sample_4d_data()
sample = cp.asarray([[0.1, -0.1, 10.1, 9.0]])
wanted = 999.99
actual = interpn(
points,
values,
sample,
method="nearest",
bounds_error=False,
fill_value=999.99,
)
assert_array_almost_equal(actual, wanted)